YSPH Biostatistics Seminar: “State Space Models for Longitudinal Cognitive Data"

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Yorghos Tripodis, PhD, Professor - Biostatistics, Boston University

TITLE: “State Space Models for Longitudinal Cognitive Data"

ABSTRACT: Alzheimer's disease, and other related dementia diseases, are a worsening issue with an acceleration in today's aging population. Longitudinal cognitive assessment of those suffering from dementia offers vital insight into disease progression and allows for assessment of possible disease interventions. Difficulty in modeling such data arises as there are often non-linear and heterogenous patterns of decline from patient to patient. We propose the use of state space models (SSM), specifically a Local Linear Trend (LLT) model, as an alternative to the commonly used linear mixed effect models (LMEM) for longitudinal assessments. The proposed model includes the estimation of interpretable population linear effects on the outcome, while also allowing for subject-specific non-linearities in cognitive trajectories. To fit the LLT model, we utilize the traditional full likelihood estimation using the Kalman Filter and Kalman Smoother. In two separate simulation analyses, we show the advantages of the LLT models over the predominant techniques.

YSPH values inclusion and access for all participants. If you have questions about accessibility or would like to request an accommodation, please contact Charmila Fernandes at Charmila.fernandes@yale.edu. We will try to provide accommodations requested by September 5, 2024.